Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation

12/14/2022
by   Dong-Young Kim, et al.
0

Drowsiness reduces concentration and increases response time, which causes fatal road accidents. Monitoring drivers' drowsiness levels by electroencephalogram (EEG) and taking action may prevent road accidents. EEG signals effectively monitor the driver's mental state as they can monitor brain dynamics. However, calibration is required in advance because EEG signals vary between and within subjects. Because of the inconvenience, calibration has reduced the accessibility of the brain-computer interface (BCI). Developing a generalized classification model is similar to domain generalization, which overcomes the domain shift problem. Especially data augmentation is frequently used. This paper proposes a calibration-free framework for driver drowsiness state classification using manifold-level augmentation. This framework increases the diversity of source domains by utilizing features. We experimented with various augmentation methods to improve the generalization performance. Based on the results of the experiments, we found that deeper models with smaller kernel sizes improved generalizability. In addition, applying an augmentation at the manifold-level resulted in an outstanding improvement. The framework demonstrated the capability for calibration-free BCI.

READ FULL TEXT
research
09/25/2019

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

Drowsy driving is pervasive, and also a major cause of traffic accidents...
research
05/11/2020

Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy

Electroencephalogram (EEG) based brain-computer interface (BCI) systems ...
research
05/30/2021

A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG

Driver drowsiness is one of main factors leading to road fatalities and ...
research
04/15/2022

Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces

Brain-computer interface (BCI) is challenging to use in practice due to ...
research
02/22/2021

MixUp Training Leads to Reduced Overfitting and Improved Calibration for the Transformer Architecture

MixUp is a computer vision data augmentation technique that uses convex ...
research
03/26/2023

Driver Drowsiness Detection with Commercial EEG Headsets

Driver Drowsiness is one of the leading causes of road accidents. Electr...
research
10/13/2020

Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface

In this study we investigate a textural processing method of electroence...

Please sign up or login with your details

Forgot password? Click here to reset